The uniqueness in the operation of a cognitive radio is that a cognitive radio is able to sense the spectral environment over a wide frequency band and exploit this information to provide wireless links that best meet a user's communications requirements. In this context, the cognitive radio does not have primary access rights to the frequency band used. As such, it is necessary that the cognitive radio is able to dynamically detect the presence of the signals transmitted by the primary users, so that it can avoid transmitting signals in the frequency channels used by the primary users. In this regard, the primary user may be considered as devices or services which have been given the primary access rights to the said frequency band(s).
A commonly used method for sensing or detecting the presence of signals transmitted by the primary users is the cyclo-stationary based detection methods. In this context, the term cyclo-stationary refers to a property of the received signal (such as the cyclic auto-correlation or the spectral correlation density (SCD), for example), which has statistical properties that vary in time with one or more periodicities.
It is known that in order to achieve perfect cyclic auto-correlation for conventional cyclo-stationary based detection methods, an infinite number of samples of the received signal are required. However, in practice, the sampling time is a finite and limited value. As such, it is possible to obtain only a finite number of samples of the received signal. Therefore, it may be difficult to achieve perfect cyclic auto-correlation in practice. In view of the above, the performance of the conventional cyclo-stationary based detection methods may be degraded.
Further, the conventional cyclo-stationary based detection methods rely on an accurate knowledge of noise power in order to set their respective threshold values. However, in practice, it may be difficult to obtain accurate knowledge of noise power due to noise uncertainty. There are several sources of noise uncertainty, namely: (1) non-linearity properties of the components used; (2) thermal noise in the components used (which may be non-uniform and time-varying, for example); (3) noise due to transmissions by other users (for example, unintentional users who may be nearby or intentional users who may be far away). As such, the conventional cyclo-stationary based detection methods may also be vulnerable to noise uncertainty.